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## layout
## `summarise()` has grouped output by 'year', 'state', 'county'. You can override
## using the `.groups` argument.
plot1_data <- cra_assets %>%
filter(state <= 56) %>%
mutate(state = str_pad(state, width = 2, pad = "0")) %>%
group_by(year, portfolio) %>%
summarize(n = n(),
amt_us_sb_loans = sum(sum_loan_amt),
n_us_sb_loans = sum(sum_n_loans))
## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.
plot1 <- ggplot(data = plot1_data) +
geom_area(aes(x = year,
y = amt_us_sb_loans,
fill = portfolio, group = portfolio))
plot1
## WITHOUT grouping by portfolio
## Also takes forever to compile, so perhaps filter out some years?
## Also I made two plots: one with a log transform and one without.
# plot2_log <- county_acs_data |>
# filter(!is.na(sum_loan_amt), !is.na(Median_HH_Income)) |>
# ggplot() +
# geom_point(mapping = aes(x = log(sum_loan_amt),
# y = log(Median_HH_Income),
# color = year,
# text = paste0("County: ", County, "<br>Total Loan Amount: ", sum_loan_amt, "<br>Median Household Income: ", Median_HH_Income, "<br>Year:", year))) +
# labs(x = "Log of Loan Amount Sum",
# y = "Log of Median Household Income",
# title = "Loan Amounts vs. Median Household Income")
plot2 <- county_acs_data |>
filter(!is.na(sum_loan_amt), !is.na(Median_HH_Income)) |>
ggplot() +
geom_point(mapping = aes(x = sum_loan_amt,
y = Median_HH_Income,
frame = year,
# color = year,
text = paste0("County: ", County, "<br>Total Loan Amount: ", sum_loan_amt, "<br>Median Household Income: ", Median_HH_Income, "<br>Year:", year))) +
labs(x = "Loan Amount Sum",
y = "Median Household Income",
title = "Loan Amounts vs. Median Household Income")
## Warning in geom_point(mapping = aes(x = sum_loan_amt, y = Median_HH_Income, :
## Ignoring unknown aesthetics: frame and text
ggplotly(plot2,
tooltip = "text")